# M3ED Sample A sample of M3ED, the multi-robot, multi-sensor, multi-environment event camera dataset, as native `.mcap` episodes. A car, a quadrotor and a Boston Dynamics Spot carry the same sensor head: a stereo pair of Prophesee event cameras at 1280x720, a stereo pair of grayscale cameras and a color camera at 1280x800, an inertial unit and an Ouster OS1-64 LiDAR. Each episode carries every event of both event cameras as point clouds, one per 1/30 s window, beside a render of each window, with the cameras, the LiDAR scans and the ground-truth poses and depth. 3 sequences, one per platform: a city street by day, a quadrotor flight outdoors at night and an indoor stairwell, 192 seconds in all with 3,631,437,863 events, 4,802 image triplets and 1,921 LiDAR scans. **Details** - Dataset name: `m3ed-sample` - Dataset source: [https://huggingface.co/datasets/Voxel51/M3ED-Sample](https://huggingface.co/datasets/Voxel51/M3ED-Sample) - Dataset size: 22.13 GB - Dataset license: CC-BY-SA-4.0 - Tags: `multimodal, mcap, event-camera, stereo, lidar` - Supported splits: `N/A` - ZooDataset class: [`M3EDSampleDataset`](../../api/fiftyone.zoo.datasets.base.md#fiftyone.zoo.datasets.base.M3EDSampleDataset) **Example usage** Python CLI ```python import fiftyone as fo import fiftyone.zoo as foz dataset = foz.load_zoo_dataset("m3ed-sample") # The sequences with the busiest event streams view = dataset.sort_by("peak_event_rate_mev_s", reverse=True) session = fo.launch_app(dataset, view=view) ``` ```shell fiftyone zoo datasets load m3ed-sample fiftyone app launch m3ed-sample ```